{"id":"W2121258717","doi":"10.1111/j.0008-4085.2005.00289.x","title":"Using Engel curves to estimate bias in the Canadian CPI as a cost of living index","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Gender, Labor, and Family Dynamics","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Engel curve; Economics; Cost of living; Index (typography); Econometrics; Welfare; Price index; Consumer price index (South Africa); Macroeconomics; Monetary policy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01699702,0.0008293723,0.001034687,0.004801536,0.0007943223,0.003043866,0.001671647,0.001050413,0.003981901],"category_scores_gemma":[0.08865701,0.0003893436,0.001481192,0.007273873,0.002094942,0.00137983,0.001590977,0.00146271,0.0004751952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01447995,"about_ca_system_score_gemma":0.008209403,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7139241,"about_ca_topic_score_gemma":0.4853637,"domain_scores_codex":[0.9952412,0.0018191,0.0001882981,0.0006066107,0.001617147,0.0005277623],"domain_scores_gemma":[0.9703857,0.01805568,0.00364653,0.003171851,0.004422482,0.0003176745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003095466,0.00005149296,0.3039529,0.000313069,0.0009755488,0.0002931785,0.001487724,0.3328164,0.0004807027,0.1966208,0.01320545,0.1494932],"study_design_scores_gemma":[0.00005746312,0.0000670161,0.2026221,0.0002702946,0.0002418612,0.0001698476,0.0008697148,0.6683957,0.0008618585,0.1090771,0.01718374,0.0001833045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5366744,0.003775408,0.4030586,0.002504717,0.0002062675,0.0005789495,0.01005909,0.001061885,0.04208058],"genre_scores_gemma":[0.9667001,0.0007295117,0.02492848,0.0001442608,0.00002436852,0.0001447043,0.003046516,0.0001255372,0.004156535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2860759,"threshold_uncertainty_score":0.5755213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2185219959764855,"score_gpt":0.2740878609793012,"score_spread":0.05556586500281563,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}